Shape Boundary Tracking with Hidden Markov Models

  • Terry Caelli
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1876)


This paper considers a Hidden Markov Model (HMM) for shape boundary generating which can be trained to be consistent with human expert performance on such tasks. That is, shapes are defined by sequences of “shape states” each of which has a probability distribution of expected image features (feature “symbols”). The tracking procedure uses a generalization of the Viterbi method by replacing its “best-first” search by “beam-search” so allowing the procedure to consider less likely features as well in the search for optimal state sequences. Results point to the benefits of such systems as an aide for experts in depiction shape boundaries as is required, for example, in Cartography.


Hidden Markov Models symbolic descriptions of boundaries predicting human performance Viterbi Search 


  1. 1.
    C. Bregler. Learning and recognizing human dynamics in video sequences. In Proceedings of the IEEE Computer Vision and Pattern Recognition, pages 1–8, 1997.Google Scholar
  2. 2.
    A. Jain and R. Dubes, editors. Algorithms for Clustering Data. Prentice Hall, Englewood Cliffs, NJ, 1988.zbMATHGoogle Scholar
  3. 3.
    L. Rabiner. A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2):257–286, 1989.CrossRefGoogle Scholar
  4. 4.
    R. Rao and D. Ballard. Learning saccadic eye movements using multiscale spatial filters. In G. Tesauro, D Touretzky, and T. Leen, editors, Advances in Neural Information Processing Systems 7, pages 893–900. MIT Press, 1995.Google Scholar
  5. 5.
    R. Rimey and C. Brown. Selective attention as sequential behavior: Modeling eye movement with an augmented hidden markov model. University of Rochester Computer Science Technical Report, 327, 1990.Google Scholar
  6. 6.
    I. Rybak, V. Gusakova, A. Golovan, L. Podladchikova, and N. Shevtsova. A model of attention-guided visual perception and recognition. Vision Research, 38:2387–2400, 1998.CrossRefGoogle Scholar
  7. 7.
    E. Simoncelli and H. Farid. Steerable wedge filters for local orientation analysis. IEEE Transactions on Image Processing, 5(9):1377–1381, 1995.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Terry Caelli
    • 1
  1. 1.Department of Computing Science Research Institute for Multimedia Systems (RIMS)The University of AlbertaEdmontonCanada

Personalised recommendations